Artificial intelligence-integrated ‘buy now, pay later’ (BNPL) platforms are diffusing rapidly across the Middle East and North Africa (MENA), raising concerns about consumer financial vulnerability. Drawing on choice architecture, payment decoupling, and financial literacy literatures, this study examines how three platform-level features—algorithmic nudging, AI personalization intensity, and perceived ease of credit—are associated with impulsive buying tendency and downstream financial outcomes, and whether BNPL-specific financial literacy attenuates these associations. A multi-method design combined cross-sectional partial least squares structural equation modeling (N = 1247 active BNPL users in seven MENA countries) with a six-month longitudinal follow-up (N = 847, 68% retention). Algorithmic nudging was positively associated with impulsive buying tendency, which in turn was associated with elevated financial stress and longitudinal debt accumulation. The ‘loyalty trap’—a paradoxical state in which financially stressed consumers maintain high platform loyalty—is provisionally documented via piecewise longitudinal trajectories. We emphasize that this pattern is consistent with but not causally established by the present design, and we outline specific experimental and quasi-experimental research designs needed for causal identification. BNPL-specific financial literacy moderated the associations between algorithmic nudging, impulsive buying, and adverse financial outcomes, with the highest-literacy quartile exhibiting substantially attenuated debt trajectories. We discuss boundary conditions, alternative explanations, and the limits of causal inference in non-experimental panel data. Findings inform evolving BNPL regulatory frameworks in MENA, with particular relevance to nudge-transparency disclosures, contractual cooling-off periods, and credit-bureau reporting standards.

Artificial-intelligence-integrated 'buy now, pay later' (BNPL) platforms are diffusing rapidly across the Middle East and North Africa (MENA), raising concerns about consumer financial vulnerability. Drawing on choice-architecture, payment-decoupling, and financial-literacy literatures, this study examines how three platform-level features — algorithmic nudging, AI personalization intensity, and perceived ease of credit — are associated with impulsive buying tendency and downstream financial outcomes, and whether BNPL-specific financial literacy attenuates these associations. A multi-method design combined cross-sectional partial-least-squares structural-equation modeling (N = 1,247 active BNPL users in seven MENA countries) with a six-month longitudinal follow-up (N = 847, 68% retention). Algorithmic nudging was positively associated with impulsive buying tendency, which in turn was associated with elevated financial stress and longitudinal debt accumulation. AI personalization was positively associated with platform loyalty but co-varied with indirect indicators of financial risk — a pattern we describe as a loyalty trap and empirically document via piecewise longitudinal trajectories. BNPL-specific financial literacy moderated the associations between algorithmic nudging, impulsive buying, and adverse financial outcomes, with the highest-literacy quartile exhibiting substantially attenuated debt trajectories. We discuss boundary conditions, alternative explanations, and the limits of causal inference in non-experimental panel data. Findings inform evolving BNPL regulatory frameworks in MENA, with particular relevance to nudge-transparency disclosures, contractual cooling-off periods, and credit-bureau reporting standards.

This study investigates the direct and conditional effects of chatbot service quality on customer satisfaction in the Egyptian banking sector. It examines how digital literacy and trust moderate this relationship in an emerging market context characterized by heterogeneous technological readiness. Utilizing a structured questionnaire to collect data between January and August 2025 from 422 Egyptian bank customers who engaged with chatbots in the preceding six months. Hypotheses were tested using hierarchical regression and structural equation modeling (SEM). The results reveal that chatbot service quality significantly enhances customer satisfaction. Crucially, both digital literacy and trust act as positive moderators; the impact of service quality on satisfaction is amplified among users who are digitally literate and trusting. The model explains 57% of the variance in satisfaction. So; Egyptian banks should therefore adopt a dual strategy to enhance the customer-experience returns of chatbot deployment: simultaneously enhancing technical quality while implementing targeted digital literacy programs and trust-building campaigns. The evidence indicates that high-quality AI alone is insufficient when users lack digital confidence or trust. Banks should segment customers, offering simplified interfaces for novices and advanced features for tech-savvy users. Transparent communication about data protection and chatbot limitations is essential. This human-centered approach bridges Egypt’s digital divide, ensuring inclusive digital transformation that drives genuine customer satisfaction across diverse user segments. This study bridges the system-centric SERVQUAL model with the user-centric technology acceptance model (TAM). It offers unique empirical evidence from an emerging market, demonstrating that high-quality AI is insufficient for satisfaction without the concurrent development of user capabilities and institutional trust.